- AI in Software Testing: How does it work?
- The role of Machine Learning (ML)
- How to use AI for software testing
- Benefits of AI in software testing
- Challenges around AI software testing
- CoTester by TestGrid: Meet all your AI software testing challenges & improve product quality with ease & efficacy
- Frequently Asked Questions (FAQs)
As AI (artificial intelligence) accelerates and expands operations in all industries, software testing hasn’t been left behind.
- 77% of organizations are investing in artificial intelligence solutions to bolster their quality engineering. Source
The “intelligence” offered by AI has wide-ranging applications in daily activities, data analysis, operational automation, and much more. This article will explore such applications of AI in software testing, as well as best practices & innovations set to change how we fundamentally approach quality engineering as a discipline.
AI in Software Testing: How does it work?
AI enables computers to “think” and “learn.” In the realm of testing and quality assurance, AI can contribute to higher efficacy and reliability—from creating tests, integrating them in CI/CD pipelines, recording in-progress tests, analyzing results, generating reports, and everything else in between.
Much like testing automation tools used so far, AI can automate mundane and repetitive testing tasks and do so far more efficiently. For example, instead of manually writing scripts for automated tests, an AI engine will create test code if you feed it the basic variables, conditions, and acceptance criteria.
While AI is currently used at some level, its ability to supercharge testing pipelines is still being discovered. Think of everything automated testing tools can do right now. Now, think of its limitations—everything you cannot do with those tools.
AI for software testing would remove those limitations.
It would completely remove the need for direct manual involvement of developers and testers. AI can take over almost all tasks except those requiring human cognition—building business logic, crafting strategy, innovating processes, and so on.
AI software testing can move beyond creating and running tests. With the right training and adaptations, it can review test results, adjust to recent code changes, manipulate code coverage, and even decide which tests are best suited for a certain project, module, environment, or test run.
AI also holds enormous promise in making decisions based on ever-changing data and trends.
The role of Machine Learning (ML)
Where does ML come into the picture?
ML improves the capabilities of AI with algorithms the latter can use to learn. As AI absorbs more data, it can adapt to changes in the testing landscape, company priorities, and team needs.
ML research is focused on studying information and making decisions based on previous data. The AI engine absorbs data consistently to make calculated and rational decisions. Over time, the engine fine-tunes its decisions in line with the specifics of the organization using it.
For example, TestGrid’s in-house AI model, CoTester, comes pre-trained with numerous advanced software testing techniques, tools, architectures, languages, and testing frameworks such as Selenium, Appium, Cypress, Robot, Cucumber, and Webdriver. TestGrid built this engine by collating extensive datasets relevant to the global software testing ecosystem.
AI for testing can use ML to recommend practices for better code coverage, conduct better static analysis, evaluate test results, and use other metrics to find gaps in efficiency. It can also be trained to respond specifically to the skills of team members, the quality of your tech stack, code repo, and larger infrastructure.
How to use AI for software testing
Testing with AI yields positive results in most areas. Using AI engines (like CoTester), QAs can accomplish the following necessary tasks in the SDLC:
- Building contextual tests: Refined AI tools for software, such as TestGrid’s CoTester, are designed to understand user intent without too many predefined commands. QAs can type instructions using plain or natural language.
AI can comprehend tasks naturally and flexibly. No rigid syntax constraints or predefined script needed; just an intuitive experience in which you build tests from words.
Take CoTester as an example. You can train it on your team’s or company’s specific strategies by:
a. uploading user stories in different formats (PDF, Word, CSV, PPT, etc.)
b. pasting URLs of web pages to be tested. CoTester will automatically load the site and ask for the type of test case to be executed. - “Healing” tests automatically: As mentioned before, AI engines can be trained to adjust to code changes in the backend. For example, if a feature’s source code has been amended, the AI engine will update the tests in the agile pipeline to match said changes.
- Refines test prioritization and execution: AI testing software can actually predict risk levels for different modules/features of the application under test. With sufficient and contextual datasets, AI can recommend reshuffling testing efforts to the right places. Historical data and logs can also help highlight the areas most likely to fail and push tests there first.
CoTester, for instance, will give you a thorough description of every single test case. It will also present a step-by-step layout demonstrating automation workflow, one that testers can edit if necessary. - Automates multiple forms of testing: AI can take on tasks related to most kinds of testing. For example, it can enhance visual testing by making pixel-by-pixel comparisons within and between UI elements. It can also measure differences in rendering between screen sizes and resolutions.
Similarly, it can detect possible vulnerabilities in an application’s security mechanisms. Gaps in API configurations, weak passwords, unauthorized access attempts, and a lot more can be quickly and consistently detected by the right AI models. - Detects and analyzes bugs: As test runs complete and bugs pop up, AI will collate and analyze them to dig up root causes. It will evaluate the requirements, test code, execution pipelines, best practices, and other project records to find possible reasons for each bug. It can also identify failure patterns in code faster than any automation tool available right now. Faster feedback cycles also help CI/CD pipelines execute faster and run more iterations in a shorter duration.
- Simulates real-world test environments: With accurate training, AI tools can be trained to prepare the right test environments. For instance, load testing requires a surge of user logins, visits, and activity on a certain web page. Ideally, testers can simply give an AI engine the expected numbers, and it would be able to generate accurate conditions.
Similarly, AI can use analytics to estimate (close to accurate) system performance under peak loads and identify bottlenecks. These obstacles can be replicated to push the app to its limits while testing and prepare it for real-world unpredictability. - Assists with manual testing: AI engines can even help improve manual testing efforts by providing informed suggestions around current test runs and goals. During exploratory testing, for example, AI can find and notify testers about high-priority areas to focus on. It can suggest possible user scenarios and lay out test steps within seconds.
- Evaluate user feedback with sentiment analysis: Let an AI engine analyze user feedback (on the App Store or Play Store), reviews, and conversations with support teams. It can detect recurring complaints, cluster complaints by location or other demographics, and provide insights for creating better user experiences.
Benefits of AI in software testing
1. Better software quality
Using AI for software testing has only one purpose: delivering better software. Every function is designed to do exactly that.
AI detects patterns in code, finds bugs & vulnerabilities, and highlights performance issues. It helps to quickly create better test cases, which leads to more comprehensive testing. It even enhances the quality and outcomes of manual testing. Finally, it improves the automation possibilities of tasks in every kind of test.
AI testing tools like CoTester can predict potential failure points based on historical data. For example, it will highlight a certain API endpoint and recommend additional testing due to a history of operational instability. This reduces the time needed for QAs to design and prioritize testing right from the requirement stage.
Building test cases and execution pipelines based on historical and expert data directly produces better software. AI lets you test more features, run more tests in each sprint, and adjust for code changes with minimal effort. It augments every stage of the QA funnel and refines the application codebase on every parameter.
2. Ability to make data-based decisions
AI can never replace human perception, but it can automate certain decisions that count as “grunt work” for humans. For instance, if you’re not using artificial intelligence for testing, your team might be building test cases from scratch. After all, current automation tools simply cannot automate this function.
Testing with AI solves this gap via machine learning capabilities. It can learn from previous projects and adapt to create structured test cases from natural language instructions.
This ability can be expanded to verify an app’s usability, accessibility, and reliability. The right AI engines can create and optimize workflows, patterns, and tasks based on testing data, user behavior, and previous interactions with the system.
These are tasks that humans have to do, but they don’t use the best potential of our cognitive abilities. By automating them, human testers can focus on driving core business value through technical improvements.
3. Enables better human productivity
AI helps human testers make better decisions by taking care of rudimentary tasks. For instance, when our customers use CoTester to build a test case, they can manually add, remove or adjust steps. Testers become expert editors, using a generated framework to innovate and modify in minutes.
CoTester also offers a chat interface via which you can amend test cases. Make your instructions precise, feed the relevant datasets, and watch the AI model build finer modifications for nuanced testing. We use every advantage of AI-based software testing to deliver better test efficiency across the board.
4. Simplifies and accelerates test maintenance
Artificial intelligence in software testing can notably cut down on the effort required to maintain, update and re-design tests.
- AI QA testing enables “self-healing,” i.e., the test script automatically detects changes in the UI or DOM structure and adapts to match said changes. Testers no longer need to manually update scripts to manage every tiny modification.
- With accurate training, AI in testing software can rank test cases by risk, priority and relevance. AI algorithms can evaluate code changes, historical data on bugs, and risk likelihood to make these decisions. Testers can execute only the essential tests instead of triggering entire regression suites for every small change.
- AI engines can scan test suites to find obsolete tests, duplicate code or low-value test steps, cases, or plans. Based on this data, QAs can optimize their pipelines with minimal effort.
- Machine learning mechanics can be used to detect elements. Instead of using static locators like IDs or XPaths, AI models can find them based on attributes like text proximity or hierarchy. This makes the entire test suite more adaptable to UI modifications.
- If a team has added a new feature, the AI engine can automatically detect said change, create/update regression tests, and fit them into the existing suite. Consequently, testers spend much less time coding as the application grows in functionality and aesthetic appeal.
- By analyzing logs, Xpaths, screenshots, and videos, AI engines can quickly find the root cause of bugs. It can even recommend strategies for resolving these bugs. Testers can debug faster and redesign tests/test data/environments with much less effort.
- Configure the AI tool to focus on testing high-traffic features. When doing so, it can study user analytics and suggest adjustments to test strategies and scope. For example, AI can highlight rarely used features (in real-world usage) and recommend reducing test coverage to optimize coding effort.
5. Increases test coverage and speed
As mentioned above, AI tools inspect requirements, user stories, and code. The data helps it build test cases automatically, including certain edge cases that manual testers can overlook.
It finds application features not adequately tested (such as rare code paths) and quickly builds tests to verify their functions—even if said functions are complex and layered.
Additionally, testers with no coding experience can create tests with natural language. They can refer to documents or conversations to create simple, understandable instructions and get complete test cases within seconds.
Needless to say, this increases test coverage with practically no increase in QA effort.
Finally, since AI is literally a thinking machine, it will execute all functions, especially test creation and running, at a speed humans cannot match.
In other words, teams can create and run a larger number of expertly designed tests within the same and shorter durations. It guarantees better software quality without any delays in time-to-market.
6. Serious cost savings
AI-enabled testing can significantly reduce costs by automating most mundane tasks. Tests are built faster with less human input, so teams have to hire only a few testers to get the job done. Additionally, AI can take over much of the test maintenance and help achieve wider test coverage.
All this translates to lower needs for human resources, except for foundational and innovative tasks.
Whatever upfront costs you’ll invest for using AI in software testing will be easily offset by the resources you’ll save. For instance, CoTester ensures that all data uploaded for training remains secure and isolated within your organization’s instance. Nothing is shared between deployments, ensuring your proprietary information remains confidential.
That’s one thing off your security team’s list. You can function with a lean team without compromising data integrity and privacy.
7. Improved team collaboration
Thanks to AI-based testing, team members with no technical training can create and run test cases. This gives the entire team a closer understanding of the product under development. Hands-on knowledge of the actual software puts every team member, tester or otherwise, on the same page.
You’ll have a more connected team with ready knowledge of what they are building. At the end of the SDLC, expect better collaboration, creative solutions, and a more well-rounded product.
Challenges around AI software testing
1. Not enough quality data
AI models must be trained on massive volumes of high-quality data that is properly tagged and labeled. If the data is unavailable or not adequately organized, your AI tool will generate inaccurate predictions and make imbalanced decisions. It will, especially, not be able to predict or recognize edge cases.
As explained above, you can train CoTester on your organization’s project history and strategies by uploading files or pasting URLs for testing. If you choose the former option, you will need a classified, correlated, and tabulated database of files. Creating such a database is no small feat.
2. Issues around transparency
Advanced AI models, such as ones for deep learning, generally aren’t too transparent about the inner mechanisms that drive their functioning. They are essentially “black boxes.”.
Consequently, testers may not understand exactly why the AI engine is highlighting a bug, predicting an outcome, prioritizing a test case, or recommending a certain course of action.
This can lower trust in the AI engine’s decision-making capabilities and make a team unwilling to move forward with AI software testing tools.
3. Integration bottlenecks
Depending on an organization’s existing tools and workflows, AI tools for software testing might face issues integrating properly into essential processes. This is especially true for DevOps pipelines, CI/CD workflows, and protocols for manual testing.
It is not uncommon that exhaustive customization is needed to incorporate AI-powered capabilities into legacy test management frameworks.
4. Skill Gaps
Any team seeking to work with AI testing requires a baseline knowledge of AI, data analysis & interpretation, and machine learning. Not every QA professional will have the requisite knowledge, which poses difficulties in using the AI engine. There may be issues with configuring, refining, and interpreting AI models and their predictions.
Companies may need to invest in training their teams to use AI and machine learning in software testing. The challenge lies in bridging the gap between traditional software testing skills and expertise around AI.
5. Significant initial costs
AI testing can be expensive to implement, at least initially. Expect some budgetary pressures for purchasing tools, training employees, and customizing system integrations.
This can create issues for smaller teams and companies, especially if setup and implementation will take a while to generate adequate ROI.
6. Adapting to evolving applications
Certain AI tools may struggle to adapt to and operate with apps that are quickly expanding with new features and functions. This is especially true if the changes add new app behaviors and user scenarios.
Considering Agile development mandates frequent changes, your chosen AI testing tool should adapt quickly to evolving requirements or environments without needing too many re-training funnels.
7. Questions around Regulation & Compliance
Certain industries, such as finance, healthcare, aviation, etc., are bound by stringent regulations. Some of these regulations can limit AI testing operations, especially concerning transparency and accountability guidelines.
Once again, the question is of training. To leverage the best of AI-enabled testing, teams need to configure AI tools to comply with industry-specific regulations.
8. Issues with scalability
Not every AI tool will scale at par with the complexity and size of the software being tested. In some cases, AI-based testing can degrade as the app grows in size and implements increasingly complex functions.
If your AI tool is trained on a small codebase, you will almost inevitably face these problems. The tool will struggle with performance and accuracy while accomplishing tasks or making predictions.
9. Ethical Questions
If training is inaccurate or insufficient, AI engines can push biases into the algorithms and training data. For instance, it may prioritize specific test cases due to organizational problems in the historical data. It can miss critical scenarios and derail entire test pipelines, hampering the fairness and reliability of decisions.
10. Fragmented Tool Market
Since the emergence of AI technology, many solutions have incorporated it into their offerings. There are multiple AI-powered software testing tools to choose from, but only some of them will be the right fit for your team, SDLC, Agile protocols, or budgetary constraints.
One tool will excel in visual testing, while another is the market leader in API testing. It can be a hassle to figure out which tool meets all or most of your company’s needs.
CoTester by TestGrid: Meet all your AI software testing challenges & improve product quality with ease & efficacy
Here’s why CoTester stands out.
This AI tester is onboardable, trainable, and taskable. It can easily integrate into your team, adapt to your processes, and perform software testing tasks just like an experienced human tester.
CoTester is specifically designed to boost your team’s efficiency, so they can focus on designing complex test scenarios, debugging anomalies the machine cannot understand, and innovating to improve product quality.
This is the world’s first AI-powered software tester pre-trained on advanced software testing methodologies, strategies, SDLC protocols, and best practices.
You can think of this domain-specific expert system as purpose-built for software testing, like ChatGPT built for QA professionals.
CoTester can:
- examine test scenarios and user stories you feed into the system
- write test cases for your website and web app
- execute tests on real browsers and devices of your choice
- help testers debug on the go
- participate in sprints
- take notes
- provide test summaries with actionable items
…all without requiring any extensive retraining or overhauling processes from the ground up.
We’ve built CoTester to assist QA engineers, freshers in automation testing, and Agile teams. It would be like having an extra brain with encyclopedic domain knowledge that is also widely adaptable to immediate test scenarios.
Frequently Asked Questions (FAQs)
How is AI used in testing?
In the realm of software testing, AI tools can (and do) take on a plethora of necessary tasks:
– Automatically creates test cases by analyzing requirements, user stories, and historical data.
– Optimizes tests by identifying the most critical areas to test based on previous test failures and the resulting impact.
– Predicts which code sections are most likely to throw up bugs.
– Adjusts test code to match changes in the UI or any other software module.
– Enables creation of test cases in natural language, i.e., plain English or any other language.
– Determines the root causes of defects by analyzing stack traces, test logs, and patterns in code.
How will AI change software testing?
AI will reorient the entire software testing ecosystem with a number of empowered capabilities:
– Enables the automation of complex test scenarios—ones that have not been automated so far.
– Designs better test cases by building them based on massive historical datasets.
– Evaluate test coverage rates and recommend ways to expand them as much as possible.
– Finds root causes of bugs faster.
– Uses sentiment analysis to assess user feedback and pinpoint potential gaps in usability.
Which AI tool is used for automation testing?
A few AI tools that go a long way in improving automated tests within your SDLC are:
– CoTester by Testgrid
– Testim
– Applitools
– Functionize
– Mabl
– Katalon Studio
– SauceLabs
– Tricentis Tosca
Will ChatGPT replace QA?
No. As useful as AI tools may be, there are certain attributes they cannot replicate:
– Human judgement and critical thinking
– Exploratory testing
– Creation of innovative test cases
– Understanding business context, nuances of particular software, and user preferences.
– Collaborating and mediating between teams.